Enhancing Legal Operations with AI Legal Co-pilot

About the Company

onegen.ai developed legal ai co pilot application

AI Legal Co Pilot Onegen AI

Industry: Global Consumer Goods Manufacturer

Annual Revenue: $20B

Locations: 50 offices worldwide

Employees: 100,000

The client is a global consumer goods manufacturer with a vast array of products sold worldwide. Despite its market leadership, the company faced significant operational challenges within its legal department due to rising legal fees and onboarding difficulties for new hires.


The legal department encountered several critical issues:

  • Soaring Legal Costs: Legal and operational costs skyrocketed from $20M in 2022 to $50M in 2023. This dramatic increase strained the department’s budget, limiting its ability to invest in other crucial areas.
  • Complex Knowledge Transfer: New hires faced a steep learning curve. The sheer volume and complexity of existing contracts, along with the intricacies of the company’s legal frameworks, made it challenging for them to become productive quickly. This led to prolonged onboarding times and decreased overall efficiency.
  • Manual Inefficiencies: The reliance on manual processes for contract reviews, compliance checks, and document management resulted in significant inefficiencies. These processes were not only time-consuming but also prone to errors, delaying response times for both internal and external queries.
  • Compliance Risks: With the increasing regulatory demands and the complexity of international operations, the company struggled to ensure all contracts and legal documents met compliance standards, exposing the firm to potential legal and financial risks.
  • Resource Constraints: The legal department was under-resourced in terms of both staff and technology, making it difficult to handle the growing workload and complexity of legal tasks effectively.

These challenges significantly impacted the department’s ability to operate smoothly, risking not only financial losses but also potential legal liabilities and a decrease in client satisfaction.

Project objectives

Reduce operational costs by optimizing legal workflows.

Improve knowledge transfer processes for new hires.

Enhance overall efficiency and response times for legal queries.

Ensure compliance with regulatory standards to mitigate legal risks.

Our approach

OneGen AI implemented its AI Legal Co-pilot solution to address these challenges. The solution included:


  • Document Analysis and Summarization: AI models to analyze and summarize existing contracts and legal documents.
  • Knowledge Base Creation: Development of a centralized, searchable knowledge base for quick access to legal frameworks and contract details.
  • Workflow Automation: Automation of routine legal tasks such as contract reviews and compliance checks.

Reliability concerns

There are valid concerns about the ability of AI to handle complex legal matters. However, Onegen AI’s Legal Co-pilot utilizes machine learning algorithms trained on massive datasets of legal documents and case law. These algorithms are constantly refined through iterative improvement processes that incorporate human feedback and utilize reinforcement learning techniques


  • Accuracy: The AI models achieve high accuracy rates in document analysis and compliance checks, reducing human error and ensuring consistency.
  • Compliance: The system is designed to comply with all legal standards and regulations, providing a robust framework for legal operations.
  • Security: Data confidentiality and integrity are top priorities. The platform utilizes state-of-the-art security protocols, including encryption and secure data repositories, to safeguard sensitive legal information.


$15M in potential annual savings by reducing legal and operational costs.

70% improvement in onboarding time for new hires.

50% increase in overall efficiency and reduction in response times for legal queries.

Solution Architecture

Data Sources:

Internal Data: Existing contracts, legal frameworks, case history.
External Data: Legal databases, regulatory information.

Platform Components:

Data Integration Layer: Aggregates and cleans data from all sources.

AI Integration:

  • ChatAI: Interacts with users to provide answers and guidance on legal queries using fine-tuned llm.
  • Draft: Facilitates the creation of legal documents based on predefined templates and rules.
  • Review: Checks documents for compliance and consistency, summarizing and highlighting areas of concern.
  • Workspace: Securely stores and organizes documents with advanced search functionality.
  • User Interface: Provides dashboards for real-time monitoring and decision-making.

Operational Flow:

  • Data Collection: Gather data from internal and external sources.
  • Data Processing: Clean and aggregate data for analysis.
  • Model Training: Train AI models using historical data.
  • Prediction and Analysis: Generate real-time insights and summaries.
  • Visualization: Display insights through user-friendly dashboards.

Project highlights

Integrated data from the company’s documents and external legal databases.

Fine-tuned large language model for document analysis and summarization.

Developed & deployed the AI Legal Co-pilot for real-time use by the legal team.

Technical details

The platform utilizes React.js and Next.js for a dynamic and responsive front-end, coupled with a backend powered by Python and Node.js for robust data processing. It employs PostgreSQL for relational data storage and Qdrant for handling high-dimensional vector data, enhancing querying capabilities. AWS S3 ensures secure and scalable storage for extensive legal datasets and documents.

The system’s core is driven by the Falcon 180B model, trained on AWS SageMaker across diverse legal texts, which supports complex legal analytics and document handling tasks. The architecture supports high availability and secure data access, conforming to legal standards, with AWS infrastructure facilitating elastic scaling and reliable deployment.


Project initiation date Demo data Delivery date
11/17/2023 01/17/2024 06/24/2024

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